Luan H. Tran

Date of Graduation


Document Type


Degree Type



Statler College of Engineering and Mineral Resources


Lane Department of Computer Science and Electrical Engineering

Committee Chair

Ali Feliachi

Committee Co-Chair

Muhammad A Choudhry

Committee Member

Natalia A Schmid


The aim of this thesis is to predict the short-term power production of PhotoVoltaic (PV) power plants for the economic dispatch problem with the help of Kalman filtering. The Economic Dispatch (ED) problem in power systems is known as an optimization problem in which the cost of producing energy to reliably supply consumers is minimized, hence the power production is assigned to all the generating units that are dispatchable. Because of the generation cost of renewable energy such as PV is relatively low, it is advantageous to utilize. However, these resources are intermittent. These renewable resources bring a lot of uncertainty into the power system, their power cannot be pre-specified due to their weather dependent properties and therefore it is a big challenge to include them in the ED problem.;For this reason, the work in this thesis will focus on developing a predictive model built on Kalman Filtering for the short-term PV prediction. The model first predicts the solar irradiance and temperature based on an initial guess at each time period. Then, the Kalman filter will refine the results using sensor measurements so that the final estimated outputs from this filter can be used for better prediction in the next period. The PV electric power is then calculated since it is a function of irradiance and temperature.;The proposed methodology has been illustrated using the IEEE 24-bus reliability test system. The real data from National Renewable Energy Laboratory is used in this thesis as the actual outputs that the outputs of the predicting model should get close to. Finally, the performance of the proposed approach is obtained by comparing its results with the results from an available method called the persistent prediction method.